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Graphical technique for identifying a monotonic variance stabilizing transformation for absolute gene intensity signals.

Archer KJ, Dumur CI, Ramakrishnan V - BMC Bioinformatics (2004)

Bottom Line: For Affymetrix data, where absolute intensities are indicative of number of transcripts, there is a systematic relationship between variance and magnitude of measurements.For the data presented, the spread-versus-level plot identified a power transformation that successfully stabilized the variance of probe set summaries.This is robust against outliers and avoids assumption of models and maximizations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298, USA. kjarcher@vcu.edu

ABSTRACT

Background: The usefulness of log2 transformation for cDNA microarray data has led to its widespread application to Affymetrix data. For Affymetrix data, where absolute intensities are indicative of number of transcripts, there is a systematic relationship between variance and magnitude of measurements. Application of the log2 transformation expands the scale of genes with low intensities while compressing the scale of genes with higher intensities thus reversing the mean by variance relationship. The usefulness of these transformations needs to be examined.

Results: Using an Affymetrix GeneChip dataset, problems associated with applying the log2 transformation to absolute intensity data are demonstrated. Use of the spread-versus-level plot to identify an appropriate variance stabilizing transformation is presented. For the data presented, the spread-versus-level plot identified a power transformation that successfully stabilized the variance of probe set summaries.

Conclusion: The spread-versus-level plot is helpful to identify transformations for variance stabilization. This is robust against outliers and avoids assumption of models and maximizations.

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Related in: MedlinePlus

Spread versus level plot. Spread-versus-level plot for 16 HG-U133A GeneChips® using MAS 5.0 probe set expression summaries; parameter estimates from least squares regression:  = 0.052,  = 0.57.
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Figure 3: Spread versus level plot. Spread-versus-level plot for 16 HG-U133A GeneChips® using MAS 5.0 probe set expression summaries; parameter estimates from least squares regression: = 0.052, = 0.57.

Mentions: In Figure 3 the spread-versus-level plot for the same data is presented. The estimated slope of the linear regression model fit to the spread-vs-level plot was = 0.569. Therefore, the estimate of the power transformation turned out to be, = 1 - 0.569 = 0.431. Typically, for simplicity the parameter estimate is rounded to the nearest half-integer, which in this case would lead to = 0.5. The family of power transformations is defined as


Graphical technique for identifying a monotonic variance stabilizing transformation for absolute gene intensity signals.

Archer KJ, Dumur CI, Ramakrishnan V - BMC Bioinformatics (2004)

Spread versus level plot. Spread-versus-level plot for 16 HG-U133A GeneChips® using MAS 5.0 probe set expression summaries; parameter estimates from least squares regression:  = 0.052,  = 0.57.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC419979&req=5

Figure 3: Spread versus level plot. Spread-versus-level plot for 16 HG-U133A GeneChips® using MAS 5.0 probe set expression summaries; parameter estimates from least squares regression: = 0.052, = 0.57.
Mentions: In Figure 3 the spread-versus-level plot for the same data is presented. The estimated slope of the linear regression model fit to the spread-vs-level plot was = 0.569. Therefore, the estimate of the power transformation turned out to be, = 1 - 0.569 = 0.431. Typically, for simplicity the parameter estimate is rounded to the nearest half-integer, which in this case would lead to = 0.5. The family of power transformations is defined as

Bottom Line: For Affymetrix data, where absolute intensities are indicative of number of transcripts, there is a systematic relationship between variance and magnitude of measurements.For the data presented, the spread-versus-level plot identified a power transformation that successfully stabilized the variance of probe set summaries.This is robust against outliers and avoids assumption of models and maximizations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298, USA. kjarcher@vcu.edu

ABSTRACT

Background: The usefulness of log2 transformation for cDNA microarray data has led to its widespread application to Affymetrix data. For Affymetrix data, where absolute intensities are indicative of number of transcripts, there is a systematic relationship between variance and magnitude of measurements. Application of the log2 transformation expands the scale of genes with low intensities while compressing the scale of genes with higher intensities thus reversing the mean by variance relationship. The usefulness of these transformations needs to be examined.

Results: Using an Affymetrix GeneChip dataset, problems associated with applying the log2 transformation to absolute intensity data are demonstrated. Use of the spread-versus-level plot to identify an appropriate variance stabilizing transformation is presented. For the data presented, the spread-versus-level plot identified a power transformation that successfully stabilized the variance of probe set summaries.

Conclusion: The spread-versus-level plot is helpful to identify transformations for variance stabilization. This is robust against outliers and avoids assumption of models and maximizations.

Show MeSH
Related in: MedlinePlus